Methodology for predicting the failure rate of mining machines with account of their multimode operation
R.N. Safiullin1, L.A. Simonova2, S.A. Lavrenko1, A.E. Pepler1, M.V. Bogdanov1
1 Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation
2 Kazan Federal University, Naberezhnye Chelny, Russian Federation
Russian Mining Industry №1/ 2026 p. 164-169
Abstract: The paper presents a methodology for forecasting operational failures of vehicles that was developed based on provisions of the probability theory, as well as methods of the mathematical and simulation modelling. This methodology differs from the known ones by a more accurate determination of the moment in time when the operational failure of the vehicle takes place with account of the cumulative mileage of mining machine for each discrete period of time within the simulation model depending on the operational factors that characterise the design, operational properties and operating conditions of the vehicle for their subsequent accounting when designing an adequate model for recovery of the mining machine's technical condition. The proposed structural model to forecast the failure rate of the mining machines taking into account the results of processing big sets of data on technical condition will allow to quantitatively assess the performance indicators of the company's repair system of mining equipment. The results obtained with the help of this model are used in research activities on technical and economic justification of technical requirements for the prospective system to restore mining equipment during operation in order to create the necessary conditions for ensuring their high availability.
Keywords: mining machines, vehicles, forecasting the failure rate, operational failures, dedicated equipment, processing of big data
For citation: Safiullin R.N., Simonova L.A., Lavrenko S.A., Pepler A.E., Bogdanov M.V. Methodology for predicting the failure rate of mining machines with account of their multimode operation. Russian Mining Industry. 2026;(1):164–169. https://doi.org/10.30686/1609-9192-2026-1-164-169
Article info
Received: 27.08.2025
Revised: 20.11.2025
Accepted: 21.11.2025
Information about the authors
Ravill N. Safiullin – Dr. Sci. (Eng.), Professor, Professor of the Department of Transport and Technological Processes and Machines, Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation; https://orcid.org/0000-0002-8765-6461; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Larisa A. Simonova – Dr. Sci. (Eng.), Professor, Professor of the Department of Automation and Control, Naberezhnye Chelny Institute, Kazan Federal University, Naberezhnye Chelny, Russian Federation; https://orcid.org/0000-0002-3653-1845; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Sergey A. Lavrenko – Cand. Sci. (Eng.), Associate Professor, Head of the Department of Practical Skills and Experience, Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation; https://orcid.org/0000-0003-1760-310X, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Artem E. Pepler – Postgraduate Student of the Department of Transport and Technological Processes and Machines, Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation; https://orcid.org/0009-0005-5984-3154; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Mikhail V. Bogdanov – Cand. Sci. (Educ.), Associate Professor of the Department of Transport and Technological Processes and Machines, Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation; https://orcid.org/0000-0001-6068-7244; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
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